Crispr/Cas9 is a two-component genome engineering tool:
Guide RNA design involves
Multicrispr aism to be a gRNA design solution which is:
For on-target scoring either the Doench2014 or the Doench2016 method can be used (Doench2016 is the current standard, see e.g. Haeussler et al., 2016). For off-target analysis Bowtie (fast) as well as vcountPDict (exact) can be used. The figure below gives an overview of how multicrispr can be used, the subsequent sections below discuss the details.
Installing multicrispr is straightforward:
# From BioC
install.packages("BiocManager")
BiocManager::install(version='devel')
BiocManager::install("multicrispr")
# From gitlab:
#url <- 'https://gitlab.gwdg.de/loosolab/software/multicrispr.git'
#remotes::install_git(url, repos = BiocManager::repositories())
Doench et al. (2016) ’s python package azimuth for on-target efficiency prediction using their method can be easily installed and activated using reticulate:
# Install once
# reticulate::conda_create('azienv', 'python=2.7')
# reticulate::conda_install('azienv', 'azimuth', pip = TRUE)
# reticulate::conda_install('azienv', 'scikit-learn==0.17.1', pip = TRUE)
# Then activate
reticulate::use_condaenv('azienv')
Bowtie-indexed genomes for fast offtarget analysis can be installed using index_genome
. For the two genomes used in the examples, mm10 and hg38, the function downloads pre-build indexes from our data server, allowing a quick exploration (set download=FALSE
to build index anew):
index_genome(BSgenome.Mmusculus.UCSC.mm10::BSgenome.Mmusculus.UCSC.mm10)
index_genome(BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38 )
bed_to_granges converts a (0-based) BED coordinate file into a (1-based) GRanges.
An example is loading the 1974 binding sites of the transcription factor SRF:
require(magrittr)
require(multicrispr)
bedfile <- system.file('extdata/SRF.bed', package = 'multicrispr')
targets0 <- bed_to_granges(bedfile, genome = 'mm10')